Human Activity Recognition: Preliminary Results for Dataset Portability using FMCW Radar

Shah, S. A. and Fioranelli, F. (2019) Human Activity Recognition: Preliminary Results for Dataset Portability using FMCW Radar. In: 2019 International Radar Conference, Toulon, France, 23-27 Sept 2019, ISBN 9781728126609 (doi: 10.1109/RADAR41533.2019.171307)

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This paper presents some preliminary results to develop a generalized system for human activity recognition (HAR) and detecting fall events using micro-Doppler signatures exploiting frequency modulated continuous wave (FMCW) radar. The core idea of this work is to demonstrate the portability and applicability of radar datasets for HAR, independent of geometrical environments and subjects involved. The experimental campaign involved different volunteers at four different geometrical locations. Two different machine learning algorithms such as support vector machine (SVM) and k-nearest neighbour (KNN), and one deep learning classifier namely GoogleNet are used to classify various human activities. The transfer learning method leveraging AlexNet algorithm is used to extract features from spectrograms to train and test the SVM and KNN classifiers. Four different scenarios are presented where datasets from three locations are combined to train and validate the classifiers, and test it on the remaining (leave-one-out) one. It is observed that the GoogleNet algorithm provides a consistent test accuracy between 68.5% to 81% for four different locations.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Shah, Mr Syed
Authors: Shah, S. A., and Fioranelli, F.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Published Online:27 April 2020
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